Omkar2118/datasets huggingface dataset:
Potsdam

https://paperswithcode.com/sota/semantic-segmentation-on-isprs-potsdam
JNLPBA
Full name: JNLPBA

**JNLPBA** is a biomedical dataset that comes from the GENIA version 3.02 corpus (Kim et al., 2003). It was created with a controlled search on MEDLINE. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. 36 terminal classes were used to annotate the GENIA corpus.
EBM-NLP

EBM-NLP annotates PICO (Participants, Interventions, Comparisons and Outcomes) spans in clinical trial abstracts. 
The corresponding PICO Extraction task aims to identify the spans in clinical trial abstracts that describe the respective PICO elements.

Introducing paper: A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature
ChemProt

**ChemProt** consists of 1,820 PubMed abstracts with chemical-protein interactions annotated by domain experts and was used in the BioCreative VI text mining chemical-protein interactions shared task.

Source: [Peng et al.](https://arxiv.org/pdf/1906.05474v2.pdf)
Paper Field
Full name: Paper Field

**Paper Field** is built from the Microsoft Academic Graph and maps paper titles to one of 7 fields of study. Each field of study - geography, politics, economics, business, sociology, medicine, and psychology - has approximately 12K training examples.
CARS196

CARS196  is composed of 16,185 car images of 196 classes.
OCHuman

This dataset focuses on heavily occluded human with comprehensive annotations including bounding-box, humans pose and instance mask. This dataset contains 13,360 elaborately annotated human instances within 5081 images. With average 0.573 MaxIoU of each person, **OCHuman** is the most complex and challenging dataset related to human.

Source: [https://github.com/liruilong940607/OCHumanApi](https://github.com/liruilong940607/OCHumanApi)
Image Source: [https://github.com/liruilong940607/OCHumanApi](https://github.com/liruilong940607/OCHumanApi)

Introducing paper: Pose2Seg: Detection Free Human Instance Segmentation
YAGO3-10
Full name: Yet Another Great Ontology 3-10

YAGO3-10 is benchmark dataset for knowledge base completion. It is a subset of YAGO3 (which itself is an extension of YAGO) that contains entities associated with at least ten different relations. In total, YAGO3-10 has 123,182 entities and 37 relations and 1,179,040 triples, and most of the triples describe attributes of persons such as citizenship, gender, and profession.
SciCite
Full name: SciCite

**SciCite** is a dataset of citation intents that addresses multiple scientific domains and is more than five times larger than ACL-ARC.

Source: [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://arxiv.org/abs/1904.01608)
Image Source: [https://arxiv.org/pdf/1904.01608v2.pdf](https://arxiv.org/pdf/1904.01608v2.pdf)

Introducing paper: Structural Scaffolds for Citation Intent Classification in Scientific Publications
CREMI

MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images.
# About
The goal of this challenge is to evaluate algorithms for automatic reconstruction of neurons and neuronal connectivity from serial section electron microscopy data. The comparison is performed not only by evaluating the quality of neuron segmentations, but also by assessing the accuracy of detecting synapses and identifying synaptic partners. The challenge is carried out on three large and diverse datasets from adult Drosophila melanogaster brain tissue, comprising neuron segmentation ground truth and annotations for synaptic connections. A successful solution would demonstrate its efficiency and generalizability, and carry great potential to reduce the time spent on manual reconstruction of neural circuits in electron microscopy volumes.

# Description
We provide three datasets, each consisting of two (5 μm)3 volumes (training and testing, each 1250 px × 1250 px × 125 px) of serial section EM of the adult fly brain. Each volume has neuron and synapse labelings and annotations for pre- and post-synaptic partners.
Watercolor2k

Watercolor2k is a dataset used for cross-domain object detection which contains 2k watercolor images with image and instance-level annotations.

Introducing paper: Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Comic2k

**Comic2k** is a dataset used for cross-domain object detection which contains 2k comic images with image and instance-level annotations.
Image Source: [https://naoto0804.github.io/cross_domain_detection/](https://naoto0804.github.io/cross_domain_detection/)

Introducing paper: Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Clipart1k

In Clipart1k, the target domain classes to be detected are the same as those in the source domain. All the images for a clipart domain were collected from one dataset (i.e., CMPlaces) and two image search engines (i.e., Openclipart2 and Pixabay3). Search queries used are 205 scene classes (e.g., pasture) used in CMPlaces to collect various objects and scenes with complex backgrounds.

Introducing paper: Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
PeopleArt
Full name: PeopleArt

People-Art is an object detection dataset which consists of people in 43 different styles. People contained in this dataset are quite different from those in common photographs. There are 42 categories of art styles and movements including Naturalism, Cubism, Socialist Realism, Impressionism, and Suprematism

Source: [Point Linking Network for Object Detection](https://arxiv.org/abs/1706.03646)
Image Source: [https://www.researchgate.net/figure/Generalization-results-on-Picasso-and-People-Art-datasets-Joseph-Redmon-2016_fig12_328175597](https://www.researchgate.net/figure/Generalization-results-on-Picasso-and-People-Art-datasets-Joseph-Redmon-2016_fig12_328175597)

Introducing paper: Detecting People in Artwork with CNNs
IconArt

This dataset contains 5955 painting images (from WikiCommons) : a train set of 2978 images and a test set of 2977 images (for classification task). 1480 of the 2977 images are annotated with bounding boxes for 7 iconographic classes : ‘angel’,‘Child_Jesus’,‘crucifixion_of_Jesus’,‘Mary’,‘nudity’, ‘ruins’,‘Saint_Sebastien’.

The dataset IconArt dataset was introduced in the following paper : "Weakly Supervised Object Detection in Artworks" Gonthier et al. ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018.

https://wsoda.telecom-paristech.fr/ 
https://zenodo.org/record/4737435

Introducing paper: Weakly Supervised Object Detection in Artworks
RWTH-PHOENIX-Weather 2014

The signing is recorded by a stationary color camera placed in front of the sign language interpreters. Interpreters wear dark clothes in front of an artificial grey background with color transition. All recorded videos are at 25 frames per second and the size of the frames is 210 by 260 pixels. Each frame shows the interpreter box only.
RWTH-PHOENIX-Weather 2014 T

Over a period of three years (2009 - 2011) the daily news and weather forecast airings of the German public tv-station PHOENIX featuring sign language interpretation have been recorded and the weather forecasts of a subset of 386 editions have been transcribed using gloss notation. Furthermore, we used automatic speech recognition with manual cleaning to transcribe the original German speech. As such, this corpus allows to train end-to-end sign language translation systems from sign language video input to spoken language.

The signing is recorded by a stationary color camera placed in front of the sign language interpreters. Interpreters wear dark clothes in front of an artificial grey background with color transition. All recorded videos are at 25 frames per second and the size of the frames is 210 by 260 pixels. Each frame shows the interpreter box only.

Introducing paper: Neural Sign Language Translation
PPI
Full name: Protein-Protein Interactions (PPI)

protein roles—in terms of their cellular functions from
gene ontology—in various protein-protein interaction (PPI) graphs, with each graph corresponding
to a different human tissue [41]. positional gene sets are used, motif gene sets and immunological
signatures as features and gene ontology sets as labels (121 in total), collected from the Molecular
Signatures Database [34]. The average graph contains 2373 nodes, with an average degree of 28.8.

Introducing paper: Inductive Representation Learning on Large Graphs