Omkar2118/datasets huggingface dataset:
BookingDataChallenge
Full name: Booking.com Data Challenge

The dataset contains anonymised hotel checkins. The dataset contains train and test parts, the in the test part city of the last checkin is masked.  The goal is to predict this masked checkin.
Multimodal Humor Dataset
Full name: Multimodal Humor Dataset: Predicting Laughter Tracks for Sitcoms

A great number of situational comedies (sitcoms) are being regularly made and the task of adding laughter tracks to these is a critical task. Providing an ability to be able to predict whether something will be humorous to the audience is also crucial. In this project, we aim to automate this task. Towards doing so, we annotate an existing sitcom (Big Bang Theory') and use the laughter cues present to obtain a manual annotation for this show. We provide detailed analysis for the dataset design and further evaluate various state of the art baselines for solving this task. We observe that existing LSTM and BERT based networks on the text alone do not perform as well as joint text and video or only video-based networks. Moreover, it is challenging to ascertain that the words attended to while predicting laughter are indeed humorous. Our dataset and analysis provided through this paper is a valuable resource towards solving this interesting semantic and practical task. As an additional contribution, we have developed a novel model for solving this task that is a multi-modal self-attention based model that outperforms currently prevalent models for solving this task.
UJIIndoorLoc

The UJIIndoorLoc is a Multi-Building Multi-Floor indoor localization database to test Indoor Positioning System that rely on WLAN/WiFi fingerprint.
Cell-200

Cell-200 is a a dataset of synthetic fluorescence microscopy images with cell populations generated by SIMCEP. The Cell-200 dataset consists of 200,000 $64\times 64$ grayscale images. The number of cells per image ranges from 1 to 200 and there are 1,000 images for each cell count. However, only a subset of Cell-200 with only odd cell counts and 10 images per count (1,000 training images in total) is used for the GAN training.
RealSRSet

20 real low-resolution images selected from existing datasets or downloaded from internet

Introducing paper: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
ACRE
Full name: Abstract Causal REasoning

Abstract Causal REasoning (ACRE) is a dataset for the systematic evaluation of current vision systems in causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables.

Each split of the dataset is structured as follows:

```
config/
    train.json
    val.json
    test.json
images/
    ACRE_train_00*.png
    ACRE_val_00*.png
    ACRE_test_00*.png
scenes/
    ACRE_train_00*.json
    ACRE_val_00*.json
    ACRE_test_00*.json
```
Each image file in the images folder has a corresponding scene description file in scenes with the same name (except for the extension).

Each ACRE problem is named after `ACRE_{train/val/test}_{6_digit_problem_idx}_{2_digit_panel_idx}`

Introducing paper: ACRE: Abstract Causal REasoning Beyond Covariation
P-OCT
Full name: Peripapillary OCT Images

The entire dataset consists of 61 different subjects, for each of which 12 radial OCT B-scans are collected at the Ophthalmology Department of Shanghai General Hospital by using DRI OCT-1 Atlantis (Topcon Corporation, Tokyo, Japan). The image size is 1024 × 992 pixels, corresponding to a field of view of 20.48 mm × 7.94 mm. For each subject, 2 radial OCT B-scans were randomly selected to ensure mutual exclusion. Two graders annotated these images manually through ITK-SNAP software into the optic disc and nine retinal layers under the supervision of a glaucoma specialist.
For more details, please refer to our [paper](https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-4-2204).
N-MNIST
Full name: Neuromorphic-MNIST

Brief Description
The Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset. It consists of the same 60 000 training and 10 000 testing samples as the original MNIST dataset, and is captured at the same visual scale as the original MNIST dataset (28x28 pixels). The N-MNIST dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views MNIST examples on an LCD monitor as shown in this video. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.

Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N.  “Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades", Frontiers in Neuroscience, vol.9, no.437, Oct. 2015
SBCoseg
Full name: SBCoseg Dataset

The SBCoseg dataset includes 889 groups of images and each group consists of 18 images with a common object, leading to 16002 images in total. The whole dataset is divided into five subsets: with ECFB, with TR, with MH, with SD, and Normal (normal data). The five subsets contain 193, 251, 82, 83, and 280 image groups, respectively. Each original image is in JPG format with a pixel size of 360 ×360, and each ground-truth image is in PNG format.
TCR-pMHC
Full name: 10x Genomics T cell receptor peptide-MHC pairs

10x Genomics dataset of sequenced TCRs barcoded by a panel of pMHCs (arranged on a dextramer)

Source: [A new way of exploring immunity: linking highly 
multiplexed antigen recognition to immune repertoire 
and phenotype](https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploring-immunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-and-phenotype/)
TCR-CMV
Full name: T cell repertoires labelled by CMV serostatus

Adaptive Biotechnologies' dataset of sequenced T cell repertoires labelled by patient age, HLA type, and CMV serostatus

Source: [Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T-cell repertoire](https://www.nature.com/articles/ng.3822)
ArtDL
Full name: ArtDL

ArtDL is a novel painting data set for iconography classification composed of images collected from online sources. Most of the paintings are from the Renaissance period and depict scenes or characters of Christian art. The data set is annotated with classes representing specific characters belonging to the Iconclass classification system.

Introducing paper: A Data Set and a Convolutional Model for Iconography Classification in Paintings
INSTRE

**INSTRE** is a benchmark for INSTance-level visual object REtrieval and REcognition (INSTRE). INSTRE has the following major properties: (1) balanced data scale,
(2) more diverse intraclass instance variations, (3) cluttered and less contextual backgrounds, (4) object localization annotation for each image, (5) well-manipulated double-labelled images for measuring multiple object (within one image) case.

The whole dataset is split into three disjoint subsets INSTRE-S1 (for single object case 1), INSTRE-S2 (for single object case 2) and INSTRE-M (for multiple object case). INSTRE-S1 and INSTRE-S2 are collected for measuring single object case, both of which have 100 object classes. INSTRE-S1 contains 11011 images and INSTRE-S2 contains 12059 images.